This course has been the toughest course I’ve ever taken in my life, ever! I’ll just say that. It includes so much material that it could be divided into 2 courses at least. I did what people suggest and went through the labs before the semester started. I spent 30-35 hours per week.
First week we were asked to complete a certification in order to handle data ethically. That took me 2 full days (16 hours) alone, and it represented maybe 1 point of the overall grade. That was only the tip of the iceberg of what was about to come.
We were assigned homeworks every 2 weeks. At the time I was taking another course too (CS 7637 Knowledge-Based Artificial Intelligence: Cognitive Systems). That’s why I had to crash the homework to finish it even earlier, so I could start working on the other course assignments. Most of the times I finished Big Data homeworks in 10 days, and 5 days left were what I got left for KBAI and I had to do magic (long nights) in those 5 days to be able to deliver homework before deadline.
Homeworks were long and covered many many topics: Hadoop, MapReduce, HDFS, Hive, Zeppelin, HBase, Pig, and many other tools. If you are into taking this course I encourage you to take all the labs present in the sunlab website. Here I leave you the link to our semester’s labs. I remember professor mentioned that he was going to do a revamp of the course. Course redesign would be in summer 2018, many spring 2018 students volunteered to help, so I guess there may be some major changes in Fall 2018. Be aware of that.
There were many errors and last minute changes in the homework environments. That took a significant amount of time during homework solution. There was always a math part included in every long assignment item list.
Something to notice is that from my experience, I never saw a comment or post from professor in Piazza during homeworks. Everything was left to the TAs, and their replies took some considerable amount. People complained a lot about feedback time. Now that I’m finishing OMSCS I remember there was one course that was almost enterily TA-driven, but it almost didn’t make a difference as head TA was always around, and he had coding sessions with us and all.
I had seen CSE 6250 reputation from omscentral.com before I took the course, so I knew what I was getting into, but I was really impressed of the poor logistics. Professor finally showed up for project part of the course, which was done in the final weeks. He had a 10 min session with every team to answer any doubts on the project and give hints on our selected subjects. That was cool.
The course team project was a beast on its own. We had to select from a list of topics or select our own topic, but selecting our own would be even harder because we would be all alone with our questions and problems, so we decided to take a topic from the list. A friend who had already taken the class suggested the same. I had 2 great team mates, that alleviated things a lot. Hi Fede and Nachi if you are reading this.
By the final 2 weeks I was already stressed out, like every semester end, I just wanted the pain to end, don’t we all?, but this time it was worse, way worse, I felt burned out. This is by every right the most time consuming and difficult course from omscentral.com, no way to compare it to anything else. From my experience, it made CS 7641 Machine Learning course look like a little kid in what is related to difficulty. That and the fact that KBAI was a time consuming course, more than I expected. I’ll remember this semester as my toughest semester in whole OMSCS.
As many mention, this course is more about big data than it is about Health Care. You do need to learn some Health Care jargon, but no big deal really. So don’t be impressed by all that Health Care stuff if you haven’t don’t any Health Care related things in the past. The most important thing you need to learn is Phenotyping, and that’s about it.
In the end I got an A, I think the grade I’ve worked for the most in my life. Well, and that was my CSE 6250 experience. Hope everything gets better for Fall 2018. See ya!